A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.
Journal:
Scientific reports
Published Date:
Jul 16, 2020
Abstract
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).
Authors
Keywords
Aneuploidy
Artificial Intelligence
Biomarkers, Tumor
Brain Neoplasms
Chromosomes, Human, Pair 10
Chromosomes, Human, Pair 7
Cyclin-Dependent Kinase Inhibitor p16
Decision Trees
Female
Gene Expression
Glioblastoma
Humans
Image Interpretation, Computer-Assisted
Isocitrate Dehydrogenase
Magnetic Resonance Imaging
Male
Mutation
Preoperative Care
Retrospective Studies
Sensitivity and Specificity
X-linked Nuclear Protein